MotivationIn 2005, researchers from the French National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l’agriculture, l’alimentation et l’environnement, INRAE) started a collaboration with the French farmers' seed network Réseau Semences Paysannes (RSP) on bread wheat participatory breeding (PPB). The aims were: (1) to study on-farm management of crop diversity, (2) to develop population-varieties adapted to organic and low-inputs agriculture, (3) to co-develop tools and methods adapted to on-farm experiments. In this project, researchers and farmers' organizations needed to map the history and life cycle of the population-varieties using network formalism to represent relationships between seed lots. All this information had to be centralized and stored in a database.ResultsWe describe here SHiNeMaS (Seeds History and Network Management System) a web tool database. SHiNeMaS aims to provide useful interfaces to track seed lot history and related data (phenotyping, environment, cultural practices). Although SHiNeMaS has been developed in the context of a bread wheat participatory breeding program, the database has been designed to manage any kind and even multiple cultivated plant species. SHiNeMaS is available under Affero GPL licence and uses free technologies such as the Python language, Django framework or PostgreSQL database management system (DBMS). ConclusionWe developed SHiNeMaS, a web tool database, dedicated to the management of the history of seed lots and related data like phenotyping, environmental information and cultural practices. SHiNeMaS has been used in production in our laboratory for 5 years and farmers' organizations facilitators manage their own information in the system.

GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.

The Wheat@URGI portal has been developed to provide the international community of researchers and breeders with access to the bread wheat reference genome sequence produced by the International Wheat Genome Sequencing Consortium. Genome browsers, BLAST, and InterMine tools have been established for in-depth exploration of the genome sequence together with additional linked datasets including physical maps, sequence variations, gene expression, and genetic and phenomic data from other international collaborative projects already stored in the GnpIS information system. The portal provides enhanced search and browser features that will facilitate the deployment of the latest genomics resources in wheat improvement.

GnpIS is an information system designed to help scientists working on plants and fungi to decipher the molecular and genetic architecture of trait variations by facilitating the navigation through genetic, genomic, and phenotypic information. The purpose of the present chapter is to illustrate how users can (1) explore datasets from phenotyping experiments in order to build new datasets for studying genotype × environment interactions in traits, (2) browse into the results of other genetic analysis data such as GWAS to generate or check working hypothesis about candidate genes or to identify important alleles and germplasms for breeding programs, and (3) explore the polymorphism in specific area of the genome using InterMine, JBrowse tools embedded in the GnpIS information system.

The genome sequences of many important Triticeae species, including bread wheat ( Triticum aestivum L.) and barley ( Hordeum vulgare L.), remained uncharacterized for a long time because their high repeat content, large sizes, and polyploidy. As a result of improvements in sequencing technologies and novel analyses strategies, several of these have recently been deciphered. These efforts have generated new insights into Triticeae biology and genome organization and have important implications for downstream usage by breeders, experimental biologists, and comparative genomicists. transPLANT ( http://www.transplantdb.eu ) is an EU‐funded project aimed at constructing hardware, software, and data infrastructure for genome‐scale research in the life sciences. Since the Triticeae data are intrinsically complex, heterogenous, and distributed, the transPLANT consortium has undertaken efforts to develop common data formats and tools that enable the exchange and integration of data from distributed resources. Here we present an overview of the individual Triticeae genome resources hosted by transPLANT partners, introduce the objectives of transPLANT, and outline common developments and interfaces supporting integrated data access.

Current research in Agronomy has produced a vast amount of genomic, genetic and phenomic data. To deal with the Volume, Variety and Velocity of those data, it is necessary to first refine to candidate datasets through data discovery then to integrate them through semantic web technologies. Data discovery is an approach that allows to easily search for datasets based on keywords and metadata. The plant bioinformatic node of the Institut Francais de Bioinformatique (IFB) gives access to several public information systems hosting domain specific data. It is composed of five bioinformatics platforms : the South Green platform, the LIPM platform, the Roscoff platform ABiMS, the platform for Arthopods for Agroecosystems Arthropods and the URGI platform. The later one plays a key role in several national an international projects like the Whea Initiative. Those platforms integrate several plant genomic, genetic and phenomic data, which they need to expose in data discovery and integration systems. The distributed data discovery system need an ETL (Extraction, Transformation and Loading) based integration pipeline implemented on each platform. This ETL can either be developed from scratch or be based on existing technologies such as KarmaWeb, Talend or Open Refine. The pipeline is being developed at the URGI, and will be deployed on all IFB plant nodes. The data discovery system is based on SolR (implemented in the Transplant portal http://www.transplantdb.eu) which uses the Lucene search framework at its core for full-text indexing. Here, we will present the data discovery system architecture and the ETL solutions evaluation and comparison. Work funded by IFB investment for the future infrastructure project, IFB_Plant node.

We used nine complete genome sequences, from grape, poplar, Arabidopsis, soybean, lotus, apple, strawberry, cacao, and papaya, to investigate the paleohistory of rosid crops. We characterized an ancestral rosid karyotype, structured into 7/21 protochomosomes, with a minimal set of 6,250 ordered protogenes and a minimum physical coding gene space of 50 megabases. We also proposed ancestral karyotypes for the Caricaceae, Brassicaceae, Malvaceae, Fabaceae, Rosaceae, Salicaceae, and Vitaceae families with 9, 8, 10, 6, 12, 9, 12, and 19 protochromosomes, respectively. On the basis of these ancestral karyotypes and present-day species comparisons, we proposed a two-step evolutionary scenario based on allohexaploidization involving the newly characterized A, B, and C diploid progenitors leading to dominant (stable) and sensitive (plastic) genomic compartments in any modern rosid crops. Finally, a new user-friendly online tool, “DicotSyntenyViewer” (available from http://urgi.versailles.inra.fr/synteny-dicot), has been made available for accurate translational genomics in rosids.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Compilation of genetic maps combined to QTL meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. One of the most interesting properties of meta-QTL (or consensus QTL) is its confidence interval (IC) often shorter than IC of corresponding QTLs, decreasing the number of candidate gene to consider. As map compilation and QTL meta-analysis do not rely on genotyping raw data or trait measure, they can be easily achieved even if user holds maps from the literature or genetic databases. BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis. The fourth version of BioMercator propose additional methods and improve graphical representation of large datasets. In this version, user may import sequence and genome annotations datasets within the software in order to display and mine functional annotation related to QTL and meta-QTL. In order to improve candidates genes detection, we aim to inculde genetic association approach in the release of BioMercator. Association genetics allow to build a relationship between molecular polymorphism and phenotypic variation so, Genome-Wide Association Studies (GWAS) present a good potential for QTL's sharpening. We integrated GWAS results in Biomercator and provided new functionalities to display and exploit them.

The 1.5 Gbp/2C genome of pedunculate oak (Quercus robur) has been sequenced. A strategy was established for dealing with the challenges imposed by the sequencing of such a large, complex and highly heterozygous genome by a whole-genome shotgun (WGS) approach, without the use of costly and time-consuming methods, such as fosmid or BAC clone-based hierarchical sequencing methods. The sequencing strategy combined short and long reads. Over 49 million reads provided by Roche 454 GS-FLX technology were assembled into contigs and combined with shorter Illumina sequence reads from paired-end and mate-pair libraries of different insert sizes, to build scaffolds. Errors were corrected and gaps filled with Illumina paired-end reads and contaminants detected, resulting in a total of 17,910 scaffolds (>2 kb) corresponding to 1.34 Gb. Fifty per cent of the assembly was accounted for by 1468 scaffolds (N50 of 260 kb). Initial comparison with the phylogenetically related Prunus persica gene model indicated that genes for 84.6% of the proteins present in peach (mean protein coverage of 90.5%) were present in our assembly. The second and third steps in this project are genome annotation and the assignment of scaffolds to the oak genetic linkage map. In accordance with the Bermuda and Fort Lauderdale agreements and the more recent Toronto Statement, the oak genome data have been released into public sequence repositories in advance of publication. In this presubmission paper, the oak genome consortium describes its principal lines of work and future directions for analyses of the nature, function and evolution of the oak genome.

Modern plant genomes are diploidized paleopolyploids. We revisited grass genome paleohistory in response to the diploidization process through a detailed investigation of the evolutionary fate of duplicated blocks. Ancestrally duplicated genes can be conserved, deleted, and shuffled, defining dominant (bias toward duplicate retention) and sensitive (bias toward duplicate erosion) chromosomal fragments. We propose a new grass genome paleohistory deriving from an ancestral karyotype structured in seven protochromosomes containing 16,464 protogenes and following evolutionary rules where 1) ancestral shared polyploidizations shaped conserved dominant (D) and sensitive (S) subgenomes, 2) subgenome dominance is revealed by both gene deletion and shuffling from the S blocks, 3) duplicate deletion/movement may have been mediated by single-/double-stranded illegitimate recombination mechanisms, 4) modern genomes arose through centromeric fusion of protochromosomes, leading to functional monocentric neochromosomes, 5) the fusion of two dominant blocks leads to supradominant neochromosomes (D + D = D) with higher ancestral gene retention compared with D + S = D (i.e., fusion of blocks with opposite sensitivity) or even S + S = S (i.e., fusion of two sensitive ancestral blocks). A new user-friendly online tool named “PlantSyntenyViewer,” available at http://urgi.versailles.inra.fr/synteny-cereal, presents the refined comparative genomics data.

RNA-seq experiments are now routinely used for the large scale sequencing of transcripts. In bacteria or archaea, such deep sequencing experiments typically produce 10–50million fragments that cover most of the genome, including intergenic regions. In this context, the precise delineation of the non-coding elements is challenging. Non-coding elements include untranslated regions (UTRs) of mRNAs, independent small RNA genes (sRNAs) and transcripts produced from the antisense strand of genes (asRNA). Here we present a computational pipeline (DETR’PROK: detection of ncRNAs in prokaryotes) based on the Galaxy framework that takes as input a mapping of deep sequencing reads and performs successive steps of clustering, comparison with existing annotation and identification of transcribed non-coding fragments classified into putative 5′ UTRs, sRNAs and asRNAs. We provide a step-by-step description of the protocol using real-life example data sets from Vibrio splendidus and Escherichia coli.

Bread wheat derives from a grass ancestor structured in seven protochromosomes followed by a paleotetraploidization to reach a 12 chromosomes intermediate and a neohexaploidization (involving subgenomes A, B and D) event that finally shaped the 21 modern chromosomes. Insights into wheat syntenome in sequencing conserved orthologous set (COS) genes unravelled differences in genomic structure (such as gene conservation and diversity) and genetical landscape (such as recombination pattern) between ancestral as well as recent duplicated blocks. Contrasted evolutionary plasticity is observed where the B subgenome appears more sensitive (i.e. plastic) in contrast to A as dominant (i.e. stable) in response to the neotetraploidization and D subgenome as supra-dominant (i.e. pivotal) in response to the neohexaploidization event. Finally, the wheat syntenome, delivered through a public web interface PlantSyntenyViewer at http://urgi.versailles.inra.fr/synteny-wheat, can be considered as a guide for accelerated dissection of major agronomical traits in wheat.

Data integration is a key challenge for modern bioinformatics. It aims to provide biologists with tools to explore relevant data produced by different studies. Large-scale international projects can generate lots of heterogeneous and unrelated data. The challenge is to integrate this information with other publicly available data. Nucleotide sequencing throughput has been improved with new technologies; this increases the need for powerful information systems able to store, manage and explore data. GnpIS is a multispecies integrative information system dedicated to plant and fungi pests. It bridges genetic and genomic data, allowing researchers access to both genetic information (e.g. genetic maps, quantitative trait loci, markers, single nucleotide polymorphisms, germplasms and genotypes) and genomic data (e.g. genomic sequences, physical maps, genome annotation and expression data) for species of agronomical interest. GnpIS is used by both large international projects and plant science departments at the French National Institute for Agricultural Research. Here, we illustrate its use. Database URL: http://urgi.versailles.inra.fr/gnpis

Thanks to our partners (genome consortia), the ANR project GnpAnnot for funding, the GMOD project for tools and all members of the URGI team, who are active developers of the system. SeqFeature:Store Fast display Chado Data warehouse With the recent development of new generation sequencing technologies, a large number of genomes are sequenced leading to an increase in derived genomic data. This huge amount of data has to be stored, analyzed and searched (gene families, SNPs ...). To face this challenge, the URGI platform (http://urgi.versailles.inra.fr) provides tools to store and annotate entirely sequenced genome such as annotation pipelines, databases and user-friendly interfaces to browse and query the data. Abstract